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Traffic prediction represents one of the crucial tasks for smartly optimizingthe mobile network. The research in this topic concentrated in makingpredictions in a centralized fashion, i.e., by collecting data from thedifferent network elements. This translates to a considerable amount of energyfor data transmission and processing. In this work, we propose a novelprediction framework based on edge computing which uses datasets obtained onthe edge through a large measurement campaign. Two main Deep Learningarchitectures are designed, based on Convolutional Neural Networks (CNNs) andRecurrent Neural Networks (RNNs), and tested under different trainingconditions. In addition, Knowledge Transfer Learning (KTL) techniques areemployed to improve the performance of the models while reducing the requiredcomputational resources. Simulation results show that the CNN architecturesoutperform the RNNs. An estimation for the needed training energy is provided,highlighting KTL ability to reduce the energy footprint of the models of 60%and 90% for CNNs and RNNs, respectively. Finally, two cutting-edge explainableArtificial Intelligence techniques are employed to interpret the derivedlearning models.

Mobile Traffic Prediction at the Edge through Distributed and Transfer Learning